Proceedings Article | 8 April 2008
Proc. SPIE. 6932, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008
KEYWORDS: Safety, Aerospace engineering, Sensors, Error analysis, Reliability, Civil engineering, Finite element methods, Chemical elements, Filtering (signal processing), Surface conduction electron emitter displays
Damage identification of structures is an important task of a health monitoring system. The ability to detect damages
on-line or almost on-line will ensure the reliability and safety of structures. Analysis methodologies for structural
damage identification based on measured vibration data have received considerable attention, including the least-square
estimation (LSE), extended Kalman filter (EKF), etc. Recently, new analysis methods, referred to as the sequential non-linear
least-square estimation (SNLSE) and quadratic sum-squares error (QSSE), have been proposed for the damage
tracking of structures. In this paper, these newly proposed analysis methods will be compared with the LSE and EKF
approaches, in terms of accuracy, convergence and efficiency, for damage identification of structures based on
experimental data. A series of experimental tests using a small-scale 3-story building model have been conducted. In
these experimental tests, white noise excitations were applied to the model, and different damage scenarios were
simulated and tested. Here, the capability of the adaptive LSE, EKF, SNLSE and QSSE approaches in tracking the
structural damage are demonstrated using experimental data. The tracking results for the stiffness of all stories, based on
each approach, are compared with the stiffness predicted by the finite-element method. The advantages and drawbacks
for each damage tracking approach will be evaluated in terms of the accuracy, efficiency and practicality.
